Metrics of impulsiveness of manual chest compressions for out-of-hospital cardiopulmonary resuscitation.

Aim: Propose new metrics of impulsiveness of manual chest compressions (CCs) that account for shape and duration, separate the characteristics of the compressive part of the CC cycle from those of the recoil part, and are uncorrelated to CC depth and rate. Methods: We conducted a retrospective analysis of adult out-of-hospital cardiac arrest monitor-defibrillator recordings having CPR data. Specifically, episodes of adult patients with ≥ 1000 compressions free of leaning were examined. CCs were obtained from the depth signal of the valid episodes, and we calculated the novel metrics: compression area index (CAI), recoil area index (RAI), compression impulsiveness index (CII) and recoil impulsiveness index (RII). Generalized linear mixed-effects models and Jonckheere-Terpstra trend analyses were employed to measure differences between populations and trends, and the absolute value of Pearson's correlation coefficient |r| was used to report dependence between variables. Statistics are reported as median and interquartile range. Results: We analyzed 982,340 CCs corresponding to 453 episodes, for which we calculated their CAI, RAI and duty cycle (DC). We analyzed the metrics for various populations: age, sex, any ROSC achieved and disposition, and found that CAI was significantly different according to patient disposition and RAI relative to age and sex (p<0.05). None of the metrics was correlated strongly to depth or rate (|r| values of 0.22 or smaller), and all of them varied for CC series corresponding to the same rescuer over the course of resuscitation (ptrend<0.05). However, we observed that the metrics are not balanced, in that for any value of DC, CAI and RAI span almost their entire ranges. Conclusion: The proposed metrics correctly and completely describe manual CC waveforms, improve upon the DC, since they depend on the signal waveform, and provide additional information to current indicators of quality CPR, depth and rate. Furthermore, they allow to differentiate the compressive and recoil parts of the CC cycle, reflecting influence of the rescuer (via CAI or CII) and of the biomechanics of the patient's chest (via RAI or RII). Thus, they have the potential to contribute to better understanding CPR dynamics and, eventually, to enhanced quality of CPR practice as additional indicators of proper manual CC technique.

The Role of Chest Compressions on Ventilation during Advanced Cardiopulmonary Resuscitation.

Background: There is growing interest in the quality of manual ventilation during cardiopulmonary resuscitation (CPR), but accurate assessment of ventilation parameters remains a challenge. Waveform capnography is currently the reference for monitoring ventilation rate in intubated patients, but fails to provide information on tidal volumes and inspiration-expiration timing. Moreover, the capnogram is often distorted when chest compressions (CCs) are performed during ventilation compromising its reliability during CPR. Our main purpose was to characterize manual ventilation during CPR and to assess how CCs may impact on ventilation quality. Methods: Retrospective analysis were performed of CPR recordings fromtwo databases of adult patients in cardiac arrest including capnogram, compression depth, and airway flow, pressure and volume signals. Using automated signal processing techniques followed by manual revision, individual ventilations were identified and ventilation parameters were measured. Oscillations on the capnogram plateau during CCs were characterized, and its correlation with compression depth and airway volume was assessed. Finally, we identified events of reversed airflow caused by CCs and their effect on volume and capnogram waveform. Results: Ventilation rates were higher than the recommended 10 breaths/min in 66.7% of the cases. Variability in ventilation rates correlated with the variability in tidal volumes and other ventilatory parameters. Oscillations caused by CCs on capnograms were of high amplitude (median above 74%) and were associated with low pseudo-volumes (median 26 mL). Correlation between the amplitude of those oscillations with either the CCs depth or the generated passive volumes was low, with correlation coefficients of -0.24 and 0.40, respectively. During inspiration and expiration, reversed airflow events caused opposed movement of gases in 80% of ventilations. Conclusions: Our study confirmed lack of adherence between measured ventilation rates and the guideline recommendations, and a substantial dispersion in manual ventilation parameters during CPR. Oscillations on the capnogram plateau caused by CCs did not correlate with compression depth or associated small tidal volumes. CCs caused reversed flow during inspiration, expiration and in the interval between ventilations, sufficient to generate volume changes and causing oscillations on capnogram. Further research is warranted to assess the impact of these findings on ventilation quality during CPR.

Characterization of mechanical properties of adult chests during pre-hospital manual chest compressions through a simple viscoelastic model.

AIM: The purpose of this study was to develop a simple viscoelastic model to characterize the mechanical properties of chests during manual chest compressions in pre-hospital cardiopulmonary resuscitation (CPR). METHODS: Force and acceleration signals were extracted from CPR monitors used during pre-hospital resuscitation attempts on adult patients. Individual chest compressions were identified and segmented from the chest displacement computed using the force and acceleration. Each compression-recoil cycle was characterized by its elastic coefficient k (a measure of stiffness) and its compression and recoil damping coefficients, dc and dr, respectively (measures of viscosity). We compared the estimated and the calculated chest displacement to assess the goodness of fit of the model. We characterized the chest of patients at the beginning of CPR in relation to sex and age, and their variation as CPR progressed. RESULTS: A total of 1,156,608 chest compressions from 615 patients were analysed. Mean (95% CI) coefficient of determination R2 for the viscoelastic model was 97.9% (97.8-98.1). At the beginning of CPR, k was 104.9 N⋅cm-1 (102.0-107.8), dc was 2.868 N⋅s⋅cm-1 (2.751-2.984) and dr was 4.889 N⋅s⋅cm-1 (4.648-5.129). Damping during recoil was significantly higher than during compression. Stiffness was lower in women than in men. There were no differences in damping coefficients with sex but a higher dr with increasing age. All model coefficients decreased with compression count, with an overall decrease after 3,000 chest compressions of 34.6%, 48.8% and 37.2%, respectively. CONCLUSION: The model accurately described adult chest mechanical properties during CPR, highlighting differences between compression and recoil, sex and age, and a progressive reduction in chest stiffness and viscosity along resuscitation. Our findings may merit further investigation into whether patient-tailored and time-sensitive chest compression technique may be appropriate.

Assessment of the evolution of end-tidal carbon dioxide within chest compression pauses to detect restoration of spontaneous circulation.

BACKGROUND: Measurement of end-tidal CO2 (ETCO2) can help to monitor circulation during cardiopulmonary resuscitation (CPR). However, early detection of restoration of spontaneous circulation (ROSC) during CPR using waveform capnography remains a challenge. The aim of the study was to investigate if the assessment of ETCO2 variation during chest compression pauses could allow for ROSC detection. We hypothesized that a decay in ETCO2 during a compression pause indicates no ROSC while a constant or increasing ETCO2 indicates ROSC. METHODS: We conducted a retrospective analysis of adult out-of-hospital cardiac arrest (OHCA) episodes treated by the advanced life support (ALS). Continuous chest compressions and ventilations were provided manually. Segments of capnography signal during pauses in chest compressions were selected, including at least three ventilations and with durations less than 20 s. Segments were classified as ROSC or non-ROSC according to case chart annotation and examination of the ECG and transthoracic impedance signals. The percentage variation of ETCO2 between consecutive ventilations was computed and its average value, ΔETavg, was used as a single feature to discriminate between ROSC and non-ROSC segments. RESULTS: A total of 384 segments (130 ROSC, 254 non-ROSC) from 205 OHCA patients (30.7% female, median age 66) were analyzed. Median (IQR) duration was 16.3 (12.9,18.1) s. ΔETavg was 0.0 (-0.7, 0.9)% for ROSC segments and -11.0 (-14.1, -8.0)% for non-ROSC segments (p < 0.0001). Best performance for ROSC detection yielded a sensitivity of 95.4% (95% CI: 90.1%, 98.1%) and a specificity of 94.9% (91.4%, 97.1%) for all ventilations in the segment. For the first 2 ventilations, duration was 7.7 (6.0, 10.2) s, and sensitivity and specificity were 90.0% (83.5%, 94.2%) and 89.4 (84.9%, 92.6%), respectively. Our method allowed for ROSC detection during the first compression pause in 95.4% of the patients. CONCLUSION: Average percent variation of ETCO2 during pauses in chest compressions allowed for ROSC discrimination. This metric could help confirm ROSC during compression pauses in ALS settings.

The impact of ventilation rate on end-tidal carbon dioxide level during manual cardiopulmonary resuscitation.

AIM: Ventilation rate is a confounding factor for interpretation of end-tidal carbon dioxide (ETCO2) during cardiopulmonary resuscitation (CPR). The aim of our study was to model the effect of ventilation rate on ETCO2 during manual CPR in adult out-of-hospital cardiac arrest (OHCA). METHODS: We conducted a retrospective analysis of OHCA monitor-defibrillator files with concurrent capnogram, compression depth, transthoracic impedance and ECG. We annotated pairs of capnogram segments presenting differences in average ventilation rate and average ETCO2 value but with other influencing factors (e.g. compression rate and depth) presenting similar values within the pair. ETCO2 variation as a function of ventilation rate was adjusted through curve fitting using non-linear least squares as a measure of goodness of fit. RESULTS: A total of 141 pairs of segments from 102 patients were annotated. Each pair provided a single data point for curve fitting. The best goodness of fit yielded a coefficient of determination R2 of 0.93. Our model described that ETCO2 decays exponentially with increasing ventilation rate. The model showed no differences attributable to the airway type (endotracheal tube or supraglottic King-LT-D). CONCLUSION: Capnogram interpretation during CPR is challenging since many factors influence ETCO2. For adequate interpretation, we need to know the effect of each factor on ETCO2. Our model allows quantifying the effect of ventilation rate on ETCO2 variation. Our findings could contribute to better interpretation of ETCO2 during CPR.

Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-of-concept of a single accelerometer-based feedback algorithm.

BACKGROUND: The use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases. MATERIALS AND METHODS: The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error. RESULTS: The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively. CONCLUSIONS: The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.